| --- |
| annotations_creators: |
| - crowdsourced |
| language_creators: |
| - crowdsourced |
| language: |
| - ru |
| license: |
| - apache-2.0 |
| multilinguality: |
| - monolingual |
| pretty_name: The Corpus for the analysis of author profiling in Russian-language texts. |
| size_categories: |
| - 10K<n<100K |
| source_datasets: |
| - original |
| task_categories: |
| - text-classification |
| task_ids: |
| - multi-class-classification |
| - multi-label-classification |
| --- |
| |
| # Dataset Card for [author_profiling] |
| |
| ## Table of Contents |
| - [Dataset Description](#dataset-description) |
| - [Dataset Summary](#dataset-summary) |
| - [Supported Tasks](#supported-tasks-and-leaderboards) |
| - [Languages](#languages) |
| - [Dataset Structure](#dataset-structure) |
| - [Data Instances](#data-instances) |
| - [Data Fields](#data-instances) |
| - [Data Splits](#data-instances) |
| - [Dataset Creation](#dataset-creation) |
| - [Curation Rationale](#curation-rationale) |
| - [Source Data](#source-data) |
| - [Annotations](#annotations) |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) |
| - [Considerations for Using the Data](#considerations-for-using-the-data) |
| - [Social Impact of Dataset](#social-impact-of-dataset) |
| - [Discussion of Biases](#discussion-of-biases) |
| - [Other Known Limitations](#other-known-limitations) |
| - [Additional Information](#additional-information) |
| - [Dataset Curators](#dataset-curators) |
| - [Licensing Information](#licensing-information) |
| - [Citation Information](#citation-information) |
| - [Contributions](#contributions) |
| |
| ## Dataset Description |
| |
| - **Homepage:** https://github.com/sag111/Author-Profiling |
| - **Repository:** https://github.com/sag111/Author-Profiling |
| - **Paper:** [Needs More Information] |
| - **Leaderboard:** [Needs More Information] |
| - **Point of Contact:** [Sboev Alexander](mailto:sag111@mail.ru) |
| |
| ### Dataset Summary |
| |
| The corpus for the author profiling analysis contains texts in Russian-language which labeled for 5 tasks: |
| 1) gender -- 13448 texts with the labels, who wrote this: text female or male; |
| |
| 2) age -- 13448 texts with the labels, how old the person who wrote the text. This is a number from 12 to 80. In addition, for the classification task we added 5 age groups: 0-19; 20-29; 30-39; 40-49; 50+; |
| |
| 3) age imitation -- 8460 texts, where crowdsource authors is asked to write three texts: |
| a) in their natural manner, |
| b) imitating the style of someone younger, |
| c) imitating the style of someone older; |
| |
| 4) gender imitation -- 4988 texts, where the crowdsource authors is asked to write texts: in their origin gender and pretending to be the opposite gender; |
| |
| 5) style imitation -- 4988 texts, where crowdsource authors is asked to write a text on behalf of another person of your own gender, with a distortion of the authors usual style. |
| |
| |
| Dataset is collected sing the Yandex.Toloka service [link](https://toloka.yandex.ru/en). |
| |
| You can read the data using the following python code: |
| ``` |
| def load_jsonl(input_path: str) -> list: |
| """ |
| Read list of objects from a JSON lines file. |
| """ |
| data = [] |
| with open(input_path, 'r', encoding='utf-8') as f: |
| for line in f: |
| data.append(json.loads(line.rstrip('\n|\r'))) |
| print('Loaded {} records from {}/n'.format(len(data), input_path)) |
| |
| return data |
| |
| path_to_file = "./data/train.jsonl" |
| data = load_jsonl(path_to_file) |
| ``` |
| or you can use HuggingFace style: |
| ``` |
| from datasets import load_dataset |
|
|
| train_df = load_dataset('sagteam/author_profiling', split='train') |
| valid_df = load_dataset('sagteam/author_profiling', split='validation') |
| test_df = load_dataset('sagteam/author_profiling', split='test') |
| ``` |
| |
| #### Here are some statistics: |
| |
| 1. For Train file: |
| - No. of documents -- 9564; |
| - No. of unique texts -- 9553; |
| - Text length in characters -- min: 197, max: 2984, mean: 500.5; |
| - No. of documents written -- by men: 4704, by women: 4860; |
| - No. of unique authors -- 2344; men: 1172, women: 1172; |
| - Age of the authors -- min: 13, max: 80, mean: 31.2; |
| - No. of documents by age group -- 0-19: 813, 20-29: 4188, 30-39: 2697, 40-49: 1194, 50+: 672; |
| - No. of documents with gender imitation: 1215; without gender imitation: 2430; not applicable: 5919; |
| - No. of documents with age imitation -- younger: 1973; older: 1973; without age imitation: 1973; not applicable: 3645; |
| - No. of documents with style imitation: 1215; without style imitation: 2430; not applicable: 5919. |
| |
| 2. For Valid file: |
| - No. of documents -- 1320; |
| - No. of unique texts -- 1316; |
| - Text length in characters -- min: 200, max: 2809, mean: 520.8; |
| - No. of documents written -- by men: 633, by women: 687; |
| - No. of unique authors -- 336; men: 168, women: 168; |
| - Age of the authors -- min: 15, max: 79, mean: 32.2; |
| - No. of documents by age group -- 1-19: 117, 20-29: 570, 30-39: 339, 40-49: 362, 50+: 132; |
| - No. of documents with gender imitation: 156; without gender imitation: 312; not applicable: 852; |
| - No. of documents with age imitation -- younger: 284; older: 284; without age imitation: 284; not applicable: 468; |
| - No. of documents with style imitation: 156; without style imitation: 312; not applicable: 852. |
| |
| 3. For Test file: |
| - No. of documents -- 2564; |
| - No. of unique texts -- 2561; |
| - Text length in characters -- min: 199, max: 3981, mean: 515.6; |
| - No. of documents written -- by men: 1290, by women: 1274; |
| - No. of unique authors -- 672; men: 336, women: 336; |
| - Age of the authors -- min: 12, max: 67, mean: 31.8; |
| - No. of documents by age group -- 1-19: 195, 20-29: 1131, 30-39: 683, 40-49: 351, 50+: 204; |
| - No. of documents with gender imitation: 292; without gender imitation: 583; not applicable: 1689; |
| - No. of documents with age imitation -- younger: 563; older: 563; without age imitation: 563; not applicable: 875; |
| - No. of documents with style imitation: 292; without style imitation: 583; not applicable: 1689. |
| |
| ### Supported Tasks and Leaderboards |
| |
| This dataset is intended for multi-class and multi-label text classification. |
| |
| The baseline models currently achieve the following F1-weighted metrics scores (table): |
| |
| | Model name | gender | age_group | gender_imitation | age_imitation | style_imitation | no_imitation | average | |
| | ------------------- | ------ | --------- | ---------------- | ------------- | --------------- | ------------ | ------- | |
| | Dummy-stratified | 0.49 | 0.29 | 0.56 | 0.32 | 0.57 | 0.55 | 0.46 | |
| | Dummy-uniform | 0.49 | 0.23 | 0.51 | 0.32 | 0.51 | 0.51 | 0.43 | |
| | Dummy-most_frequent | 0.34 | 0.27 | 0.53 | 0.17 | 0.53 | 0.53 | 0.40 | |
| | LinearSVC + TF-IDF | 0.67 | 0.37 | 0.62 | 0.72 | 0.71 | 0.71 | 0.63 | |
| |
| ### Languages |
| |
| The text in the dataset is in Russian. |
| |
| ## Dataset Structure |
| |
| ### Data Instances |
| |
| Each instance is a text in Russian with some author profiling annotations. |
| |
| An example for an instance from the dataset is shown below: |
| ``` |
| { |
| 'id': 'crowdsource_4916', |
| 'text': 'Ты очень симпатичный, Я давно не с кем не встречалась. Ты мне сильно понравился, ты умный интересный и удивительный, приходи ко мне в гости , у меня есть вкусное вино , и приготовлю вкусный ужин, посидим пообщаемся, узнаем друг друга поближе.', |
| 'account_id': 'account_#1239', |
| 'author_id': 411, |
| 'age': 22, |
| 'age_group': '20-29', |
| 'gender': 'male', |
| 'no_imitation': 'with_any_imitation', |
| 'age_imitation': 'None', |
| 'gender_imitation': 'with_gender_imitation', |
| 'style_imitation': 'no_style_imitation' |
| } |
| ``` |
| |
| ### Data Fields |
| |
| Data Fields includes: |
| - id -- unique identifier of the sample; |
| |
| - text -- authors text written by a crowdsourcing user; |
| |
| - author_id -- unique identifier of the user; |
| |
| - account_id -- unique identifier of the crowdsource account; |
| |
| - age -- age annotations; |
| |
| - age_group -- age group annotations; |
| |
| - no_imitation -- imitation annotations. |
| Label codes: |
| - 'with_any_imitation' -- there is some imitation in the text; |
| - 'no_any_imitation' -- the text is written without any imitation |
| |
| - age_imitation -- age imitation annotations. |
| Label codes: |
| - 'younger' -- someone younger than the author is imitated in the text; |
| - 'older' -- someone older than the author is imitated in the text; |
| - 'no_age_imitation' -- the text is written without age imitation; |
| - 'None' -- not supported (the text was not written for this task) |
| |
| - gender_imitation -- gender imitation annotations. |
| Label codes: |
| - 'no_gender_imitation' -- the text is written without gender imitation; |
| - 'with_gender_imitation' -- the text is written with a gender imitation; |
| - 'None' -- not supported (the text was not written for this task) |
| |
| - style_imitation -- style imitation annotations. |
| Label codes: |
| - 'no_style_imitation' -- the text is written without style imitation; |
| - 'with_style_imitation' -- the text is written with a style imitation; |
| - 'None' -- not supported (the text was not written for this task). |
| |
| ### Data Splits |
| |
| The dataset includes a set of train/valid/test splits with 9564, 1320 and 2564 texts respectively. |
| The unique authors do not overlap between the splits. |
| |
| ## Dataset Creation |
| |
| ### Curation Rationale |
| |
| The formed dataset of examples consists of texts in Russian using a crowdsourcing platform. The created dataset can be used to improve the accuracy of supervised classifiers in author profiling tasks. |
| |
| ### Source Data |
| |
| #### Initial Data Collection and Normalization |
| |
| Data was collected from crowdsource platform. Each text was written by the author specifically for the task provided. |
| |
| #### Who are the source language producers? |
| |
| Russian-speaking Yandex.Toloka users. |
| |
| ### Annotations |
| |
| #### Annotation process |
| |
| We used a crowdsourcing platform to collect texts. Each respondent is asked to fill a questionnaire including their gender, age and native language. |
| |
| For age imitation task the respondents are to choose a |
| topic out of a few suggested, and write three texts on it: |
| 1) Text in their natural manner; |
| 2) Text imitating the style of someone younger; |
| 3) Text imitating the style of someone older. |
| |
| For gender and style imitation task each author wrote three texts in certain different styles: |
| 1) Text in the authors natural style; |
| 2) Text imitating other gender style; |
| 3) Text in a different style but without gender imitation. |
| |
| The topics to choose from are the following. |
| - An attempt to persuade some arbitrary listener to meet the respondent at their place; |
| - A story about some memorable event/acquisition/rumour or whatever else the imaginary listener is supposed to enjoy; |
| - A story about oneself or about someone else, aiming to please the listener and win their favour; |
| - A description of oneself and one’s potential partner for a dating site; |
| - An attempt to persuade an unfamiliar person to come; |
| - A negative tour review. |
| |
| The task does not pass checking and is considered improper work if it contains: |
| - Irrelevant answers to the questionnaire; |
| - Incoherent jumble of words; |
| - Chunks of text borrowed from somewhere else; |
| - Texts not conforming to the above list of topics. |
| |
| Texts checking is performed firstly by automated search for borrowings (by an anti-plagiarism website), and then by manual review of compliance to the task. |
| |
| #### Who are the annotators? |
| |
| Russian-speaking Yandex.Toloka users. |
| |
| ### Personal and Sensitive Information |
| |
| All personal data was anonymized. Each author has been assigned an impersonal, unique identifier. |
| |
| ## Considerations for Using the Data |
| |
| ### Social Impact of Dataset |
| |
| [Needs More Information] |
| |
| ### Discussion of Biases |
| |
| [Needs More Information] |
| |
| ### Other Known Limitations |
| |
| [Needs More Information] |
| |
| ## Additional Information |
| |
| ### Dataset Curators |
| |
| Researchers at AI technology lab at NRC "Kurchatov Institute". See the [website](https://sagteam.ru/). |
| |
| ### Licensing Information |
| |
| Apache License 2.0. |
| |
| ### Citation Information |
| |
| If you have found our results helpful in your work, feel free to cite our publication. |
| ``` |
| @article{сбоев2022сравнение, |
| title={СРАВНЕНИЕ ТОЧНОСТЕЙ МЕТОДОВ НА ОСНОВЕ ЯЗЫКОВЫХ И ГРАФОВЫХ НЕЙРОСЕТЕВЫХ МОДЕЛЕЙ ДЛЯ ОПРЕДЕЛЕНИЯ ПРИЗНАКОВ АВТОРСКОГО ПРОФИЛЯ ПО ТЕКСТАМ НА РУССКОМ ЯЗЫКЕ}, |
| author={Сбоев, АГ and Молошников, ИА and Рыбка, РБ and Наумов, АВ and Селиванов, АА}, |
| journal={Вестник Национального исследовательского ядерного университета МИФИ}, |
| volume={10}, |
| number={6}, |
| pages={529--539}, |
| year={2021}, |
| publisher={Общество с ограниченной ответственностью МАИК "Наука/Интерпериодика"} |
| } |
| ``` |
| ### Contributions |
| |
| Thanks to [@naumov-al](https://github.com/naumov-al) for adding this dataset. |
| |
